Two-Timescale Geometric Model of Genetic Cognition in Systems Theory: Quantizing Evolutionary Inference and Behavioral Distributions under Genetic, Cognitive, and Environmental Constraint

This paper introduces a unified geometric framework for modeling cognition and evolution as interacting optimization processes operating on different timescales. Rather than treating behavior, cognition, and evolution as separate domains, the model describes all three as movement within a shared constrained state‑space shaped by genetic structure, environmental curvature, and frictional cost. At the core of the framework is the idea that behavior is not generated from free choice or preference but emerges as biased sampling from a landscape whose geometry is inherited from evolutionary structure. Genes define fixed attractor wells that update only across discrete evolutionary “turns” (successful reproduction), while cognition (“ego*”) performs continuous local optimization within a lifetime. Environmental conditions modulate curvature and friction, altering which trajectories are easy, costly, or unstable. A central contribution of the paper is the introduction of genetic time as a quantized variable. Genetic state advances only when reproductive events produce viable offspring, creating a turn‑based inference loop at the population level. This formalizes how slow evolutionary redistribution and fast cognitive navigation co‑shape behavioral distributions without invoking intent, preference, or teleology. The model further explains behavioral misalignment as a geometric effect: in low‑friction environments, local reward mechanisms can stabilize trajectories that are locally optimal for ego* but weakly coupled—or uncoupled—from long‑horizon genetic persistence. This degeneracy arises without dysfunction or error and reflects optimization under shallow constraints. Overall, this work provides a minimal structural substrate for modeling behavior, cognition, and evolution as components of a single optimization geometry. It introduces no normative assumptions and no psychological or moral claims; instead, it offers a reductionist language for systems modeling, classification, predictive heuristics, and future formal derivations across cognitive science, evolutionary dynamics, and decision theory.